Overview

Dataset statistics

Number of variables29
Number of observations3756
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory851.1 KiB
Average record size in memory232.0 B

Variable types

Numeric17
Categorical12

Alerts

director_name has a high cardinality: 1659 distinct values High cardinality
actor_2_name has a high cardinality: 2188 distinct values High cardinality
genres has a high cardinality: 745 distinct values High cardinality
actor_1_name has a high cardinality: 1428 distinct values High cardinality
movie_title has a high cardinality: 3655 distinct values High cardinality
actor_3_name has a high cardinality: 2587 distinct values High cardinality
plot_keywords has a high cardinality: 3656 distinct values High cardinality
movie_imdb_link has a high cardinality: 3656 distinct values High cardinality
df_index is highly correlated with gross and 1 other fieldsHigh correlation
num_critic_for_reviews is highly correlated with num_voted_users and 2 other fieldsHigh correlation
actor_3_facebook_likes is highly correlated with actor_1_facebook_likes and 2 other fieldsHigh correlation
actor_1_facebook_likes is highly correlated with actor_3_facebook_likes and 2 other fieldsHigh correlation
gross is highly correlated with df_index and 3 other fieldsHigh correlation
num_voted_users is highly correlated with num_critic_for_reviews and 2 other fieldsHigh correlation
cast_total_facebook_likes is highly correlated with actor_3_facebook_likes and 2 other fieldsHigh correlation
num_user_for_reviews is highly correlated with num_critic_for_reviews and 2 other fieldsHigh correlation
budget is highly correlated with df_index and 1 other fieldsHigh correlation
title_year is highly correlated with num_critic_for_reviewsHigh correlation
actor_2_facebook_likes is highly correlated with actor_3_facebook_likes and 2 other fieldsHigh correlation
num_critic_for_reviews is highly correlated with num_voted_users and 2 other fieldsHigh correlation
actor_3_facebook_likes is highly correlated with actor_2_facebook_likesHigh correlation
actor_1_facebook_likes is highly correlated with cast_total_facebook_likesHigh correlation
gross is highly correlated with num_voted_users and 1 other fieldsHigh correlation
num_voted_users is highly correlated with num_critic_for_reviews and 3 other fieldsHigh correlation
cast_total_facebook_likes is highly correlated with actor_1_facebook_likes and 1 other fieldsHigh correlation
num_user_for_reviews is highly correlated with num_critic_for_reviews and 2 other fieldsHigh correlation
actor_2_facebook_likes is highly correlated with actor_3_facebook_likes and 1 other fieldsHigh correlation
movie_facebook_likes is highly correlated with num_critic_for_reviews and 1 other fieldsHigh correlation
df_index is highly correlated with budgetHigh correlation
num_critic_for_reviews is highly correlated with num_voted_users and 1 other fieldsHigh correlation
actor_3_facebook_likes is highly correlated with cast_total_facebook_likes and 1 other fieldsHigh correlation
actor_1_facebook_likes is highly correlated with cast_total_facebook_likes and 1 other fieldsHigh correlation
num_voted_users is highly correlated with num_critic_for_reviews and 1 other fieldsHigh correlation
cast_total_facebook_likes is highly correlated with actor_3_facebook_likes and 2 other fieldsHigh correlation
num_user_for_reviews is highly correlated with num_critic_for_reviews and 1 other fieldsHigh correlation
budget is highly correlated with df_indexHigh correlation
actor_2_facebook_likes is highly correlated with actor_3_facebook_likes and 2 other fieldsHigh correlation
language is highly correlated with countryHigh correlation
country is highly correlated with languageHigh correlation
df_index is highly correlated with grossHigh correlation
num_critic_for_reviews is highly correlated with gross and 4 other fieldsHigh correlation
duration is highly correlated with countryHigh correlation
director_facebook_likes is highly correlated with languageHigh correlation
actor_3_facebook_likes is highly correlated with gross and 3 other fieldsHigh correlation
actor_1_facebook_likes is highly correlated with cast_total_facebook_likesHigh correlation
gross is highly correlated with df_index and 4 other fieldsHigh correlation
num_voted_users is highly correlated with num_critic_for_reviews and 5 other fieldsHigh correlation
cast_total_facebook_likes is highly correlated with actor_3_facebook_likes and 2 other fieldsHigh correlation
num_user_for_reviews is highly correlated with num_critic_for_reviews and 3 other fieldsHigh correlation
language is highly correlated with director_facebook_likes and 2 other fieldsHigh correlation
country is highly correlated with duration and 2 other fieldsHigh correlation
content_rating is highly correlated with title_yearHigh correlation
budget is highly correlated with language and 1 other fieldsHigh correlation
title_year is highly correlated with num_critic_for_reviews and 1 other fieldsHigh correlation
actor_2_facebook_likes is highly correlated with actor_3_facebook_likes and 1 other fieldsHigh correlation
imdb_score is highly correlated with num_voted_users and 1 other fieldsHigh correlation
movie_facebook_likes is highly correlated with num_critic_for_reviews and 1 other fieldsHigh correlation
actor_1_facebook_likes is highly skewed (γ1 = 20.3394708) Skewed
budget is highly skewed (γ1 = 44.17414414) Skewed
movie_title is uniformly distributed Uniform
actor_3_name is uniformly distributed Uniform
plot_keywords is uniformly distributed Uniform
movie_imdb_link is uniformly distributed Uniform
df_index has unique values Unique
director_facebook_likes has 642 (17.1%) zeros Zeros
facenumber_in_poster has 1582 (42.1%) zeros Zeros
movie_facebook_likes has 1742 (46.4%) zeros Zeros

Reproduction

Analysis started2022-05-11 15:02:11.244909
Analysis finished2022-05-11 15:03:56.565576
Duration1 minute and 45.32 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct3756
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2139.449414
Minimum0
Maximum5042
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size29.5 KiB
2022-05-11T20:33:56.870505image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile193.75
Q1989.75
median2033.5
Q33163.25
95-th percentile4538.25
Maximum5042
Range5042
Interquartile range (IQR)2173.5

Descriptive statistics

Standard deviation1345.761978
Coefficient of variation (CV)0.6290225743
Kurtosis-0.9680602618
Mean2139.449414
Median Absolute Deviation (MAD)1085.5
Skewness0.2735491187
Sum8035772
Variance1811075.302
MonotonicityStrictly increasing
2022-05-11T20:33:57.229869image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
< 0.1%
27871
 
< 0.1%
27711
 
< 0.1%
27731
 
< 0.1%
27741
 
< 0.1%
27761
 
< 0.1%
27771
 
< 0.1%
27791
 
< 0.1%
27801
 
< 0.1%
27811
 
< 0.1%
Other values (3746)3746
99.7%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
101
< 0.1%
ValueCountFrequency (%)
50421
< 0.1%
50351
< 0.1%
50331
< 0.1%
50271
< 0.1%
50261
< 0.1%
50251
< 0.1%
50151
< 0.1%
50121
< 0.1%
50111
< 0.1%
50081
< 0.1%

color
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size29.5 KiB
Color
3632 
Black and White
 
124

Length

Max length16
Median length5
Mean length5.36315229
Min length5

Characters and Unicode

Total characters20144
Distinct characters16
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowColor
2nd rowColor
3rd rowColor
4th rowColor
5th rowColor

Common Values

ValueCountFrequency (%)
Color3632
96.7%
Black and White124
 
3.3%

Length

2022-05-11T20:33:57.542365image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-11T20:33:57.839231image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
color3632
90.7%
black124
 
3.1%
and124
 
3.1%
white124
 
3.1%

Most occurring characters

ValueCountFrequency (%)
o7264
36.1%
l3756
18.6%
C3632
18.0%
r3632
18.0%
372
 
1.8%
a248
 
1.2%
B124
 
0.6%
c124
 
0.6%
k124
 
0.6%
n124
 
0.6%
Other values (6)744
 
3.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter15892
78.9%
Uppercase Letter3880
 
19.3%
Space Separator372
 
1.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o7264
45.7%
l3756
23.6%
r3632
22.9%
a248
 
1.6%
c124
 
0.8%
k124
 
0.8%
n124
 
0.8%
d124
 
0.8%
h124
 
0.8%
i124
 
0.8%
Other values (2)248
 
1.6%
Uppercase Letter
ValueCountFrequency (%)
C3632
93.6%
B124
 
3.2%
W124
 
3.2%
Space Separator
ValueCountFrequency (%)
372
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin19772
98.2%
Common372
 
1.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
o7264
36.7%
l3756
19.0%
C3632
18.4%
r3632
18.4%
a248
 
1.3%
B124
 
0.6%
c124
 
0.6%
k124
 
0.6%
n124
 
0.6%
d124
 
0.6%
Other values (5)620
 
3.1%
Common
ValueCountFrequency (%)
372
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII20144
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o7264
36.1%
l3756
18.6%
C3632
18.0%
r3632
18.0%
372
 
1.8%
a248
 
1.2%
B124
 
0.6%
c124
 
0.6%
k124
 
0.6%
n124
 
0.6%
Other values (6)744
 
3.7%

director_name
Categorical

HIGH CARDINALITY

Distinct1659
Distinct (%)44.2%
Missing0
Missing (%)0.0%
Memory size29.5 KiB
Steven Spielberg
 
25
Clint Eastwood
 
19
Woody Allen
 
19
Ridley Scott
 
17
Martin Scorsese
 
16
Other values (1654)
3660 

Length

Max length32
Median length24
Mean length13.03541001
Min length3

Characters and Unicode

Total characters48961
Distinct characters74
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique942 ?
Unique (%)25.1%

Sample

1st rowJames Cameron
2nd rowGore Verbinski
3rd rowSam Mendes
4th rowChristopher Nolan
5th rowAndrew Stanton

Common Values

ValueCountFrequency (%)
Steven Spielberg25
 
0.7%
Clint Eastwood19
 
0.5%
Woody Allen19
 
0.5%
Ridley Scott17
 
0.5%
Martin Scorsese16
 
0.4%
Steven Soderbergh16
 
0.4%
Tim Burton16
 
0.4%
Spike Lee15
 
0.4%
Renny Harlin15
 
0.4%
Ron Howard13
 
0.3%
Other values (1649)3585
95.4%

Length

2022-05-11T20:33:58.205316image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
john147
 
1.9%
david116
 
1.5%
michael97
 
1.2%
peter75
 
1.0%
james69
 
0.9%
robert68
 
0.9%
paul66
 
0.8%
steven56
 
0.7%
richard55
 
0.7%
scott54
 
0.7%
Other values (2125)6995
89.7%

Most occurring characters

ValueCountFrequency (%)
e4751
 
9.7%
4042
 
8.3%
a3836
 
7.8%
n3551
 
7.3%
r3390
 
6.9%
o2922
 
6.0%
i2754
 
5.6%
l2227
 
4.5%
t1797
 
3.7%
s1569
 
3.2%
Other values (64)18122
37.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter36692
74.9%
Uppercase Letter7974
 
16.3%
Space Separator4042
 
8.3%
Other Punctuation191
 
0.4%
Dash Punctuation62
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e4751
12.9%
a3836
10.5%
n3551
9.7%
r3390
 
9.2%
o2922
 
8.0%
i2754
 
7.5%
l2227
 
6.1%
t1797
 
4.9%
s1569
 
4.3%
h1384
 
3.8%
Other values (29)8511
23.2%
Uppercase Letter
ValueCountFrequency (%)
S781
 
9.8%
J703
 
8.8%
M670
 
8.4%
R590
 
7.4%
C538
 
6.7%
B503
 
6.3%
D461
 
5.8%
A415
 
5.2%
L381
 
4.8%
P380
 
4.8%
Other values (21)2552
32.0%
Other Punctuation
ValueCountFrequency (%)
.176
92.1%
'15
 
7.9%
Space Separator
ValueCountFrequency (%)
4042
100.0%
Dash Punctuation
ValueCountFrequency (%)
-62
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin44666
91.2%
Common4295
 
8.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e4751
 
10.6%
a3836
 
8.6%
n3551
 
8.0%
r3390
 
7.6%
o2922
 
6.5%
i2754
 
6.2%
l2227
 
5.0%
t1797
 
4.0%
s1569
 
3.5%
h1384
 
3.1%
Other values (60)16485
36.9%
Common
ValueCountFrequency (%)
4042
94.1%
.176
 
4.1%
-62
 
1.4%
'15
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII48870
99.8%
None91
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e4751
 
9.7%
4042
 
8.3%
a3836
 
7.8%
n3551
 
7.3%
r3390
 
6.9%
o2922
 
6.0%
i2754
 
5.6%
l2227
 
4.6%
t1797
 
3.7%
s1569
 
3.2%
Other values (46)18031
36.9%
None
ValueCountFrequency (%)
é23
25.3%
á15
16.5%
ö13
14.3%
ó11
12.1%
å6
 
6.6%
ñ5
 
5.5%
ç4
 
4.4%
í3
 
3.3%
Ô2
 
2.2%
æ1
 
1.1%
Other values (8)8
 
8.8%

num_critic_for_reviews
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct525
Distinct (%)14.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean167.378328
Minimum2
Maximum813
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size29.5 KiB
2022-05-11T20:33:58.557580image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile28
Q177
median138.5
Q3224
95-th percentile417
Maximum813
Range811
Interquartile range (IQR)147

Descriptive statistics

Standard deviation123.4520402
Coefficient of variation (CV)0.7375628713
Kurtosis2.529591949
Mean167.378328
Median Absolute Deviation (MAD)70.5
Skewness1.424722665
Sum628673
Variance15240.40623
MonotonicityNot monotonic
2022-05-11T20:33:58.875256image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8129
 
0.8%
9823
 
0.6%
11122
 
0.6%
6322
 
0.6%
9422
 
0.6%
11222
 
0.6%
9722
 
0.6%
6021
 
0.6%
7521
 
0.6%
6121
 
0.6%
Other values (515)3531
94.0%
ValueCountFrequency (%)
23
 
0.1%
42
 
0.1%
52
 
0.1%
61
 
< 0.1%
71
 
< 0.1%
84
0.1%
98
0.2%
108
0.2%
117
0.2%
129
0.2%
ValueCountFrequency (%)
8131
< 0.1%
7751
< 0.1%
7651
< 0.1%
7502
0.1%
7391
< 0.1%
7381
< 0.1%
7331
< 0.1%
7231
< 0.1%
7121
< 0.1%
7032
0.1%

duration
Real number (ℝ≥0)

HIGH CORRELATION

Distinct151
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean110.2579872
Minimum37
Maximum330
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size29.5 KiB
2022-05-11T20:33:59.219000image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum37
5-th percentile85
Q196
median106
Q3120
95-th percentile148
Maximum330
Range293
Interquartile range (IQR)24

Descriptive statistics

Standard deviation22.64671656
Coefficient of variation (CV)0.2053975148
Kurtosis12.62739704
Mean110.2579872
Median Absolute Deviation (MAD)12
Skewness2.402551754
Sum414129
Variance512.8737711
MonotonicityNot monotonic
2022-05-11T20:33:59.593988image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
101113
 
3.0%
100104
 
2.8%
98103
 
2.7%
9595
 
2.5%
9993
 
2.5%
10792
 
2.4%
10691
 
2.4%
9090
 
2.4%
9790
 
2.4%
11088
 
2.3%
Other values (141)2797
74.5%
ValueCountFrequency (%)
371
 
< 0.1%
451
 
< 0.1%
531
 
< 0.1%
631
 
< 0.1%
661
 
< 0.1%
682
 
0.1%
691
 
< 0.1%
723
0.1%
732
 
0.1%
745
0.1%
ValueCountFrequency (%)
3301
< 0.1%
3251
< 0.1%
3001
< 0.1%
2931
< 0.1%
2891
< 0.1%
2801
< 0.1%
2711
< 0.1%
2512
0.1%
2401
< 0.1%
2361
< 0.1%

director_facebook_likes
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct395
Distinct (%)10.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean807.3365282
Minimum0
Maximum23000
Zeros642
Zeros (%)17.1%
Negative0
Negative (%)0.0%
Memory size29.5 KiB
2022-05-11T20:33:59.937728image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q111
median64
Q3235
95-th percentile2000
Maximum23000
Range23000
Interquartile range (IQR)224

Descriptive statistics

Standard deviation3068.171683
Coefficient of variation (CV)3.800362767
Kurtosis22.25900267
Mean807.3365282
Median Absolute Deviation (MAD)64
Skewness4.754529215
Sum3032356
Variance9413677.474
MonotonicityNot monotonic
2022-05-11T20:34:00.328347image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0642
 
17.1%
640
 
1.1%
740
 
1.1%
1139
 
1.0%
1338
 
1.0%
1236
 
1.0%
1035
 
0.9%
2334
 
0.9%
831
 
0.8%
1100031
 
0.8%
Other values (385)2790
74.3%
ValueCountFrequency (%)
0642
17.1%
224
 
0.6%
330
 
0.8%
430
 
0.8%
526
 
0.7%
640
 
1.1%
740
 
1.1%
831
 
0.8%
931
 
0.8%
1035
 
0.9%
ValueCountFrequency (%)
230001
 
< 0.1%
220008
 
0.2%
2100010
 
0.3%
180004
 
0.1%
1700016
0.4%
1600027
0.7%
150002
 
0.1%
1400029
0.8%
1300019
0.5%
1200017
0.5%

actor_3_facebook_likes
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct882
Distinct (%)23.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean771.2795527
Minimum0
Maximum23000
Zeros27
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size29.5 KiB
2022-05-11T20:34:00.656464image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile24
Q1194
median436
Q3691
95-th percentile1000
Maximum23000
Range23000
Interquartile range (IQR)497

Descriptive statistics

Standard deviation1894.249869
Coefficient of variation (CV)2.455983518
Kurtosis45.77112024
Mean771.2795527
Median Absolute Deviation (MAD)249
Skewness6.370113892
Sum2896926
Variance3588182.567
MonotonicityNot monotonic
2022-05-11T20:34:01.000206image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1000116
 
3.1%
1100028
 
0.7%
027
 
0.7%
300026
 
0.7%
200025
 
0.7%
400017
 
0.5%
1000016
 
0.4%
82615
 
0.4%
74814
 
0.4%
32214
 
0.4%
Other values (872)3458
92.1%
ValueCountFrequency (%)
027
0.7%
28
 
0.2%
38
 
0.2%
413
0.3%
56
 
0.2%
69
 
0.2%
713
0.3%
89
 
0.2%
96
 
0.2%
105
 
0.1%
ValueCountFrequency (%)
230002
 
0.1%
200001
 
< 0.1%
190005
 
0.1%
170001
 
< 0.1%
160003
 
0.1%
150001
 
< 0.1%
140006
 
0.2%
130005
 
0.1%
120008
 
0.2%
1100028
0.7%

actor_2_name
Categorical

HIGH CARDINALITY

Distinct2188
Distinct (%)58.3%
Missing0
Missing (%)0.0%
Memory size29.5 KiB
Morgan Freeman
 
20
Charlize Theron
 
14
Brad Pitt
 
14
James Franco
 
11
Meryl Streep
 
10
Other values (2183)
3687 

Length

Max length28
Median length25
Mean length13.08892439
Min length3

Characters and Unicode

Total characters49162
Distinct characters78
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1460 ?
Unique (%)38.9%

Sample

1st rowJoel David Moore
2nd rowOrlando Bloom
3rd rowRory Kinnear
4th rowChristian Bale
5th rowSamantha Morton

Common Values

ValueCountFrequency (%)
Morgan Freeman20
 
0.5%
Charlize Theron14
 
0.4%
Brad Pitt14
 
0.4%
James Franco11
 
0.3%
Meryl Streep10
 
0.3%
Jason Flemyng10
 
0.3%
Adam Sandler9
 
0.2%
Will Ferrell9
 
0.2%
Bruce Willis9
 
0.2%
Angelina Jolie Pitt9
 
0.2%
Other values (2178)3641
96.9%

Length

2022-05-11T20:34:01.343949image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
michael70
 
0.9%
tom41
 
0.5%
james41
 
0.5%
jason40
 
0.5%
david39
 
0.5%
scott39
 
0.5%
robert34
 
0.4%
john32
 
0.4%
adam31
 
0.4%
thomas31
 
0.4%
Other values (2926)7391
94.9%

Most occurring characters

ValueCountFrequency (%)
e4631
 
9.4%
a4364
 
8.9%
4033
 
8.2%
n3571
 
7.3%
r3269
 
6.6%
i3068
 
6.2%
o2750
 
5.6%
l2588
 
5.3%
t1748
 
3.6%
s1625
 
3.3%
Other values (68)17515
35.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter36939
75.1%
Uppercase Letter8000
 
16.3%
Space Separator4033
 
8.2%
Other Punctuation141
 
0.3%
Dash Punctuation45
 
0.1%
Decimal Number4
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e4631
12.5%
a4364
11.8%
n3571
9.7%
r3269
8.8%
i3068
 
8.3%
o2750
 
7.4%
l2588
 
7.0%
t1748
 
4.7%
s1625
 
4.4%
h1333
 
3.6%
Other values (36)7992
21.6%
Uppercase Letter
ValueCountFrequency (%)
M752
 
9.4%
C613
 
7.7%
S609
 
7.6%
B565
 
7.1%
J559
 
7.0%
D500
 
6.2%
R450
 
5.6%
A446
 
5.6%
L382
 
4.8%
T358
 
4.5%
Other values (16)2766
34.6%
Other Punctuation
ValueCountFrequency (%)
.93
66.0%
'48
34.0%
Decimal Number
ValueCountFrequency (%)
02
50.0%
52
50.0%
Space Separator
ValueCountFrequency (%)
4033
100.0%
Dash Punctuation
ValueCountFrequency (%)
-45
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin44939
91.4%
Common4223
 
8.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e4631
 
10.3%
a4364
 
9.7%
n3571
 
7.9%
r3269
 
7.3%
i3068
 
6.8%
o2750
 
6.1%
l2588
 
5.8%
t1748
 
3.9%
s1625
 
3.6%
h1333
 
3.0%
Other values (62)15992
35.6%
Common
ValueCountFrequency (%)
4033
95.5%
.93
 
2.2%
'48
 
1.1%
-45
 
1.1%
02
 
< 0.1%
52
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII49075
99.8%
None87
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e4631
 
9.4%
a4364
 
8.9%
4033
 
8.2%
n3571
 
7.3%
r3269
 
6.7%
i3068
 
6.3%
o2750
 
5.6%
l2588
 
5.3%
t1748
 
3.6%
s1625
 
3.3%
Other values (48)17428
35.5%
None
ValueCountFrequency (%)
é27
31.0%
í13
14.9%
á8
 
9.2%
ë6
 
6.9%
å5
 
5.7%
ø5
 
5.7%
ü3
 
3.4%
ö3
 
3.4%
ó3
 
3.4%
ï2
 
2.3%
Other values (10)12
13.8%

actor_1_facebook_likes
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct713
Distinct (%)19.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7751.338658
Minimum0
Maximum640000
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size29.5 KiB
2022-05-11T20:34:01.656436image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile210
Q1745
median1000
Q313000
95-th percentile26000
Maximum640000
Range640000
Interquartile range (IQR)12255

Descriptive statistics

Standard deviation15519.33962
Coefficient of variation (CV)2.002149603
Kurtosis757.7504015
Mean7751.338658
Median Absolute Deviation (MAD)942.5
Skewness20.3394708
Sum29114028
Variance240849902.3
MonotonicityNot monotonic
2022-05-11T20:34:01.992804image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1000348
 
9.3%
11000190
 
5.1%
2000166
 
4.4%
3000137
 
3.6%
12000122
 
3.2%
13000112
 
3.0%
14000110
 
2.9%
18000104
 
2.8%
1000097
 
2.6%
2200071
 
1.9%
Other values (703)2299
61.2%
ValueCountFrequency (%)
01
 
< 0.1%
25
0.1%
32
 
0.1%
53
0.1%
62
 
0.1%
72
 
0.1%
91
 
< 0.1%
111
 
< 0.1%
152
 
0.1%
171
 
< 0.1%
ValueCountFrequency (%)
6400001
 
< 0.1%
2600001
 
< 0.1%
1640001
 
< 0.1%
1370002
 
0.1%
870007
 
0.2%
4900025
0.7%
460001
 
< 0.1%
450005
 
0.1%
440002
 
0.1%
4000039
1.0%

gross
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3638
Distinct (%)96.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52612824.24
Minimum162
Maximum760505847
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size29.5 KiB
2022-05-11T20:34:02.336550image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum162
5-th percentile196022.25
Q18270232.75
median30093107
Q366881940.75
95-th percentile186762606.5
Maximum760505847
Range760505685
Interquartile range (IQR)58611708

Descriptive statistics

Standard deviation70317866.91
Coefficient of variation (CV)1.336515725
Kurtosis13.97005326
Mean52612824.24
Median Absolute Deviation (MAD)25209842
Skewness3.029374712
Sum1.976137678 × 1011
Variance4.944602407 × 1015
MonotonicityNot monotonic
2022-05-11T20:34:02.649042image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1773436753
 
0.1%
57735193
 
0.1%
80000003
 
0.1%
2180512603
 
0.1%
349648183
 
0.1%
1445123103
 
0.1%
470000003
 
0.1%
243436732
 
0.1%
4039322
 
0.1%
586070072
 
0.1%
Other values (3628)3729
99.3%
ValueCountFrequency (%)
1621
< 0.1%
7031
< 0.1%
7211
< 0.1%
11111
< 0.1%
13321
< 0.1%
24361
< 0.1%
24681
< 0.1%
25801
< 0.1%
29641
< 0.1%
34781
< 0.1%
ValueCountFrequency (%)
7605058471
< 0.1%
6586723021
< 0.1%
6521772711
< 0.1%
6232795472
0.1%
5333160611
< 0.1%
4745446771
< 0.1%
4609356651
< 0.1%
4589915991
< 0.1%
4481306421
< 0.1%
4364710361
< 0.1%

genres
Categorical

HIGH CARDINALITY

Distinct745
Distinct (%)19.8%
Missing0
Missing (%)0.0%
Memory size29.5 KiB
Comedy|Drama|Romance
 
147
Drama
 
141
Comedy|Drama
 
138
Comedy
 
138
Comedy|Romance
 
131
Other values (740)
3061 

Length

Max length64
Median length52
Mean length21.22018104
Min length5

Characters and Unicode

Total characters79703
Distinct characters32
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique393 ?
Unique (%)10.5%

Sample

1st rowAction|Adventure|Fantasy|Sci-Fi
2nd rowAction|Adventure|Fantasy
3rd rowAction|Adventure|Thriller
4th rowAction|Thriller
5th rowAction|Adventure|Sci-Fi

Common Values

ValueCountFrequency (%)
Comedy|Drama|Romance147
 
3.9%
Drama141
 
3.8%
Comedy|Drama138
 
3.7%
Comedy138
 
3.7%
Comedy|Romance131
 
3.5%
Drama|Romance115
 
3.1%
Crime|Drama|Thriller82
 
2.2%
Action|Crime|Thriller56
 
1.5%
Action|Crime|Drama|Thriller50
 
1.3%
Action|Adventure|Sci-Fi48
 
1.3%
Other values (735)2710
72.2%

Length

2022-05-11T20:34:03.102154image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
comedy|drama|romance147
 
3.9%
drama141
 
3.8%
comedy|drama138
 
3.7%
comedy138
 
3.7%
comedy|romance131
 
3.5%
drama|romance115
 
3.1%
crime|drama|thriller82
 
2.2%
action|crime|thriller56
 
1.5%
action|crime|drama|thriller50
 
1.3%
action|adventure|sci-fi48
 
1.3%
Other values (735)2710
72.2%

Most occurring characters

ValueCountFrequency (%)
r7970
 
10.0%
|7480
 
9.4%
a6829
 
8.6%
e6255
 
7.8%
m5606
 
7.0%
i5248
 
6.6%
o4841
 
6.1%
y3611
 
4.5%
n3602
 
4.5%
t3228
 
4.1%
Other values (22)25033
31.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter59993
75.3%
Uppercase Letter11733
 
14.7%
Math Symbol7480
 
9.4%
Dash Punctuation497
 
0.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r7970
13.3%
a6829
11.4%
e6255
10.4%
m5606
9.3%
i5248
8.7%
o4841
8.1%
y3611
 
6.0%
n3602
 
6.0%
t3228
 
5.4%
l2773
 
4.6%
Other values (8)10030
16.7%
Uppercase Letter
ValueCountFrequency (%)
C2170
18.5%
D1938
16.5%
A1936
16.5%
F1446
12.3%
T1117
9.5%
R859
 
7.3%
S644
 
5.5%
M631
 
5.4%
H541
 
4.6%
B239
 
2.0%
Other values (2)212
 
1.8%
Math Symbol
ValueCountFrequency (%)
|7480
100.0%
Dash Punctuation
ValueCountFrequency (%)
-497
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin71726
90.0%
Common7977
 
10.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r7970
 
11.1%
a6829
 
9.5%
e6255
 
8.7%
m5606
 
7.8%
i5248
 
7.3%
o4841
 
6.7%
y3611
 
5.0%
n3602
 
5.0%
t3228
 
4.5%
l2773
 
3.9%
Other values (20)21763
30.3%
Common
ValueCountFrequency (%)
|7480
93.8%
-497
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII79703
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r7970
 
10.0%
|7480
 
9.4%
a6829
 
8.6%
e6255
 
7.8%
m5606
 
7.0%
i5248
 
6.6%
o4841
 
6.1%
y3611
 
4.5%
n3602
 
4.5%
t3228
 
4.1%
Other values (22)25033
31.4%

actor_1_name
Categorical

HIGH CARDINALITY

Distinct1428
Distinct (%)38.0%
Missing0
Missing (%)0.0%
Memory size29.5 KiB
Robert De Niro
 
42
Johnny Depp
 
39
J.K. Simmons
 
31
Nicolas Cage
 
31
Denzel Washington
 
30
Other values (1423)
3583 

Length

Max length27
Median length24
Mean length13.14882854
Min length4

Characters and Unicode

Total characters49387
Distinct characters72
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique870 ?
Unique (%)23.2%

Sample

1st rowCCH Pounder
2nd rowJohnny Depp
3rd rowChristoph Waltz
4th rowTom Hardy
5th rowDaryl Sabara

Common Values

ValueCountFrequency (%)
Robert De Niro42
 
1.1%
Johnny Depp39
 
1.0%
J.K. Simmons31
 
0.8%
Nicolas Cage31
 
0.8%
Denzel Washington30
 
0.8%
Bruce Willis29
 
0.8%
Matt Damon28
 
0.7%
Liam Neeson26
 
0.7%
Robert Downey Jr.26
 
0.7%
Robin Williams25
 
0.7%
Other values (1418)3449
91.8%

Length

2022-05-11T20:34:03.445895image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
robert94
 
1.2%
tom84
 
1.1%
michael63
 
0.8%
de48
 
0.6%
jason46
 
0.6%
steve45
 
0.6%
will44
 
0.6%
niro42
 
0.5%
johnny41
 
0.5%
matt41
 
0.5%
Other values (2054)7238
93.0%

Most occurring characters

ValueCountFrequency (%)
e4658
 
9.4%
a4168
 
8.4%
4030
 
8.2%
n3600
 
7.3%
r3159
 
6.4%
i3148
 
6.4%
o2933
 
5.9%
l2493
 
5.0%
t1945
 
3.9%
s1775
 
3.6%
Other values (62)17478
35.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter37155
75.2%
Uppercase Letter7982
 
16.2%
Space Separator4030
 
8.2%
Other Punctuation168
 
0.3%
Dash Punctuation50
 
0.1%
Decimal Number2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e4658
12.5%
a4168
11.2%
n3600
9.7%
r3159
 
8.5%
i3148
 
8.5%
o2933
 
7.9%
l2493
 
6.7%
t1945
 
5.2%
s1775
 
4.8%
h1337
 
3.6%
Other values (29)7939
21.4%
Uppercase Letter
ValueCountFrequency (%)
J746
 
9.3%
M656
 
8.2%
C641
 
8.0%
S617
 
7.7%
D566
 
7.1%
B533
 
6.7%
R466
 
5.8%
H401
 
5.0%
W383
 
4.8%
L370
 
4.6%
Other values (17)2603
32.6%
Other Punctuation
ValueCountFrequency (%)
.140
83.3%
'28
 
16.7%
Decimal Number
ValueCountFrequency (%)
51
50.0%
01
50.0%
Space Separator
ValueCountFrequency (%)
4030
100.0%
Dash Punctuation
ValueCountFrequency (%)
-50
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin45137
91.4%
Common4250
 
8.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e4658
 
10.3%
a4168
 
9.2%
n3600
 
8.0%
r3159
 
7.0%
i3148
 
7.0%
o2933
 
6.5%
l2493
 
5.5%
t1945
 
4.3%
s1775
 
3.9%
h1337
 
3.0%
Other values (56)15921
35.3%
Common
ValueCountFrequency (%)
4030
94.8%
.140
 
3.3%
-50
 
1.2%
'28
 
0.7%
51
 
< 0.1%
01
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII49332
99.9%
None55
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e4658
 
9.4%
a4168
 
8.4%
4030
 
8.2%
n3600
 
7.3%
r3159
 
6.4%
i3148
 
6.4%
o2933
 
5.9%
l2493
 
5.1%
t1945
 
3.9%
s1775
 
3.6%
Other values (48)17423
35.3%
None
ValueCountFrequency (%)
ë14
25.5%
é14
25.5%
á5
 
9.1%
å4
 
7.3%
í4
 
7.3%
ç3
 
5.5%
à2
 
3.6%
ü2
 
3.6%
ø2
 
3.6%
ö1
 
1.8%
Other values (4)4
 
7.3%

movie_title
Categorical

HIGH CARDINALITY
UNIFORM

Distinct3655
Distinct (%)97.3%
Missing0
Missing (%)0.0%
Memory size29.5 KiB
Home 
 
3
Pan 
 
3
King Kong 
 
3
Halloween 
 
3
Victor Frankenstein 
 
3
Other values (3650)
3741 

Length

Max length84
Median length53
Mean length16.19941427
Min length2

Characters and Unicode

Total characters60845
Distinct characters89
Distinct categories12 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3560 ?
Unique (%)94.8%

Sample

1st rowAvatar 
2nd rowPirates of the Caribbean: At World's End 
3rd rowSpectre 
4th rowThe Dark Knight Rises 
5th rowJohn Carter 

Common Values

ValueCountFrequency (%)
Home 3
 
0.1%
Pan 3
 
0.1%
King Kong 3
 
0.1%
Halloween 3
 
0.1%
Victor Frankenstein 3
 
0.1%
The Fast and the Furious 3
 
0.1%
The Island 2
 
0.1%
Dawn of the Dead 2
 
0.1%
Around the World in 80 Days 2
 
0.1%
Mercury Rising 2
 
0.1%
Other values (3645)3730
99.3%

Length

2022-05-11T20:34:03.758388image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the1204
 
11.6%
of353
 
3.4%
a132
 
1.3%
and104
 
1.0%
297
 
0.9%
in97
 
0.9%
to71
 
0.7%
60
 
0.6%
man56
 
0.5%
on43
 
0.4%
Other values (3872)8130
78.6%

Most occurring characters

ValueCountFrequency (%)
6591
 
10.8%
e5842
 
9.6%
 3756
 
6.2%
a3562
 
5.9%
o3445
 
5.7%
r3088
 
5.1%
n3064
 
5.0%
i2924
 
4.8%
t2828
 
4.6%
s2247
 
3.7%
Other values (79)23498
38.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter40252
66.2%
Space Separator10347
 
17.0%
Uppercase Letter9087
 
14.9%
Other Punctuation677
 
1.1%
Decimal Number402
 
0.7%
Dash Punctuation69
 
0.1%
Currency Symbol3
 
< 0.1%
Other Number2
 
< 0.1%
Open Punctuation2
 
< 0.1%
Close Punctuation2
 
< 0.1%
Other values (2)2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e5842
14.5%
a3562
 
8.8%
o3445
 
8.6%
r3088
 
7.7%
n3064
 
7.6%
i2924
 
7.3%
t2828
 
7.0%
s2247
 
5.6%
h2204
 
5.5%
l1885
 
4.7%
Other values (21)9163
22.8%
Uppercase Letter
ValueCountFrequency (%)
T1302
14.3%
S781
 
8.6%
M629
 
6.9%
B570
 
6.3%
D520
 
5.7%
C507
 
5.6%
A488
 
5.4%
H417
 
4.6%
L414
 
4.6%
W377
 
4.1%
Other values (17)3082
33.9%
Decimal Number
ValueCountFrequency (%)
2128
31.8%
374
18.4%
059
14.7%
156
13.9%
424
 
6.0%
517
 
4.2%
815
 
3.7%
913
 
3.2%
69
 
2.2%
77
 
1.7%
Other Punctuation
ValueCountFrequency (%)
:284
41.9%
'154
22.7%
.98
 
14.5%
,49
 
7.2%
&47
 
6.9%
!25
 
3.7%
?12
 
1.8%
/7
 
1.0%
·1
 
0.1%
Space Separator
ValueCountFrequency (%)
6591
63.7%
 3756
36.3%
Currency Symbol
ValueCountFrequency (%)
¢2
66.7%
$1
33.3%
Open Punctuation
ValueCountFrequency (%)
(1
50.0%
[1
50.0%
Close Punctuation
ValueCountFrequency (%)
)1
50.0%
]1
50.0%
Dash Punctuation
ValueCountFrequency (%)
-69
100.0%
Other Number
ValueCountFrequency (%)
½2
100.0%
Connector Punctuation
ValueCountFrequency (%)
_1
100.0%
Math Symbol
ValueCountFrequency (%)
+1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin49339
81.1%
Common11506
 
18.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e5842
 
11.8%
a3562
 
7.2%
o3445
 
7.0%
r3088
 
6.3%
n3064
 
6.2%
i2924
 
5.9%
t2828
 
5.7%
s2247
 
4.6%
h2204
 
4.5%
l1885
 
3.8%
Other values (48)18250
37.0%
Common
ValueCountFrequency (%)
6591
57.3%
 3756
32.6%
:284
 
2.5%
'154
 
1.3%
2128
 
1.1%
.98
 
0.9%
374
 
0.6%
-69
 
0.6%
059
 
0.5%
156
 
0.5%
Other values (21)237
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII57075
93.8%
None3770
 
6.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6591
 
11.5%
e5842
 
10.2%
a3562
 
6.2%
o3445
 
6.0%
r3088
 
5.4%
n3064
 
5.4%
i2924
 
5.1%
t2828
 
5.0%
s2247
 
3.9%
h2204
 
3.9%
Other values (69)21280
37.3%
None
ValueCountFrequency (%)
 3756
99.6%
é4
 
0.1%
¢2
 
0.1%
½2
 
0.1%
è1
 
< 0.1%
ñ1
 
< 0.1%
á1
 
< 0.1%
Æ1
 
< 0.1%
·1
 
< 0.1%
ü1
 
< 0.1%

num_voted_users
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3674
Distinct (%)97.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean105826.7327
Minimum91
Maximum1689764
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size29.5 KiB
2022-05-11T20:34:04.195877image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum91
5-th percentile3673.25
Q119667
median53973.5
Q3128602
95-th percentile385889
Maximum1689764
Range1689673
Interquartile range (IQR)108935

Descriptive statistics

Standard deviation152035.3993
Coefficient of variation (CV)1.436644555
Kurtosis19.98071987
Mean105826.7327
Median Absolute Deviation (MAD)41383
Skewness3.650372696
Sum397485208
Variance2.311476264 × 1010
MonotonicityNot monotonic
2022-05-11T20:34:04.492747image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36653
 
0.1%
79732
 
0.1%
1104862
 
0.1%
52542
 
0.1%
42882
 
0.1%
239282
 
0.1%
1130682
 
0.1%
286212
 
0.1%
230232
 
0.1%
129802
 
0.1%
Other values (3664)3735
99.4%
ValueCountFrequency (%)
911
< 0.1%
1541
< 0.1%
2411
< 0.1%
3441
< 0.1%
3971
< 0.1%
4481
< 0.1%
4491
< 0.1%
4751
< 0.1%
4801
< 0.1%
5241
< 0.1%
ValueCountFrequency (%)
16897641
< 0.1%
16761691
< 0.1%
14682001
< 0.1%
13474611
< 0.1%
13246801
< 0.1%
12512221
< 0.1%
12387461
< 0.1%
12177521
< 0.1%
12157181
< 0.1%
11557701
< 0.1%

cast_total_facebook_likes
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3243
Distinct (%)86.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11527.10197
Minimum0
Maximum656730
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size29.5 KiB
2022-05-11T20:34:04.977108image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile449
Q11919.75
median4059.5
Q316240
95-th percentile41428.25
Maximum656730
Range656730
Interquartile range (IQR)14320.25

Descriptive statistics

Standard deviation19122.17691
Coefficient of variation (CV)1.658888501
Kurtosis369.5087978
Mean11527.10197
Median Absolute Deviation (MAD)3113.5
Skewness12.89487374
Sum43295795
Variance365657649.6
MonotonicityNot monotonic
2022-05-11T20:34:05.961458image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
27304
 
0.1%
15204
 
0.1%
23234
 
0.1%
24
 
0.1%
11364
 
0.1%
29904
 
0.1%
23484
 
0.1%
24864
 
0.1%
10444
 
0.1%
23214
 
0.1%
Other values (3233)3716
98.9%
ValueCountFrequency (%)
01
 
< 0.1%
24
0.1%
41
 
< 0.1%
53
0.1%
61
 
< 0.1%
71
 
< 0.1%
111
 
< 0.1%
131
 
< 0.1%
152
0.1%
281
 
< 0.1%
ValueCountFrequency (%)
6567301
< 0.1%
3037171
< 0.1%
2635841
< 0.1%
1402681
< 0.1%
1377121
< 0.1%
1207971
< 0.1%
1080161
< 0.1%
1067591
< 0.1%
1033541
< 0.1%
1013831
< 0.1%

actor_3_name
Categorical

HIGH CARDINALITY
UNIFORM

Distinct2587
Distinct (%)68.9%
Missing0
Missing (%)0.0%
Memory size29.5 KiB
Steve Coogan
 
8
Ben Mendelsohn
 
7
Robert Duvall
 
7
Kirsten Dunst
 
7
Anne Hathaway
 
7
Other values (2582)
3720 

Length

Max length27
Median length24
Mean length13.05777423
Min length3

Characters and Unicode

Total characters49045
Distinct characters78
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1886 ?
Unique (%)50.2%

Sample

1st rowWes Studi
2nd rowJack Davenport
3rd rowStephanie Sigman
4th rowJoseph Gordon-Levitt
5th rowPolly Walker

Common Values

ValueCountFrequency (%)
Steve Coogan8
 
0.2%
Ben Mendelsohn7
 
0.2%
Robert Duvall7
 
0.2%
Kirsten Dunst7
 
0.2%
Anne Hathaway7
 
0.2%
Sam Shepard6
 
0.2%
Craig T. Nelson6
 
0.2%
Kevin Dunn6
 
0.2%
Stephen Root6
 
0.2%
Kevin Pollak6
 
0.2%
Other values (2577)3690
98.2%

Length

2022-05-11T20:34:06.242700image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
michael67
 
0.9%
james52
 
0.7%
david52
 
0.7%
john47
 
0.6%
robert37
 
0.5%
kevin36
 
0.5%
peter31
 
0.4%
tom31
 
0.4%
steve31
 
0.4%
scott29
 
0.4%
Other values (3336)7371
94.7%

Most occurring characters

ValueCountFrequency (%)
e4630
 
9.4%
a4442
 
9.1%
4028
 
8.2%
n3470
 
7.1%
r3123
 
6.4%
i2965
 
6.0%
o2677
 
5.5%
l2673
 
5.5%
t1754
 
3.6%
s1718
 
3.5%
Other values (68)17565
35.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter36762
75.0%
Uppercase Letter8010
 
16.3%
Space Separator4028
 
8.2%
Other Punctuation185
 
0.4%
Dash Punctuation58
 
0.1%
Decimal Number2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e4630
12.6%
a4442
12.1%
n3470
9.4%
r3123
 
8.5%
i2965
 
8.1%
o2677
 
7.3%
l2673
 
7.3%
t1754
 
4.8%
s1718
 
4.7%
h1364
 
3.7%
Other values (33)7946
21.6%
Uppercase Letter
ValueCountFrequency (%)
M758
 
9.5%
S613
 
7.7%
J611
 
7.6%
B598
 
7.5%
C587
 
7.3%
R488
 
6.1%
D477
 
6.0%
A422
 
5.3%
L381
 
4.8%
H354
 
4.4%
Other values (19)2721
34.0%
Other Punctuation
ValueCountFrequency (%)
.141
76.2%
'44
 
23.8%
Decimal Number
ValueCountFrequency (%)
51
50.0%
01
50.0%
Space Separator
ValueCountFrequency (%)
4028
100.0%
Dash Punctuation
ValueCountFrequency (%)
-58
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin44772
91.3%
Common4273
 
8.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e4630
 
10.3%
a4442
 
9.9%
n3470
 
7.8%
r3123
 
7.0%
i2965
 
6.6%
o2677
 
6.0%
l2673
 
6.0%
t1754
 
3.9%
s1718
 
3.8%
h1364
 
3.0%
Other values (62)15956
35.6%
Common
ValueCountFrequency (%)
4028
94.3%
.141
 
3.3%
-58
 
1.4%
'44
 
1.0%
51
 
< 0.1%
01
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII48950
99.8%
None95
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e4630
 
9.5%
a4442
 
9.1%
4028
 
8.2%
n3470
 
7.1%
r3123
 
6.4%
i2965
 
6.1%
o2677
 
5.5%
l2673
 
5.5%
t1754
 
3.6%
s1718
 
3.5%
Other values (48)17470
35.7%
None
ValueCountFrequency (%)
é33
34.7%
á11
 
11.6%
í7
 
7.4%
ë7
 
7.4%
à6
 
6.3%
ü5
 
5.3%
ó5
 
5.3%
è4
 
4.2%
ø3
 
3.2%
ô2
 
2.1%
Other values (10)12
 
12.6%

facenumber_in_poster
Real number (ℝ≥0)

ZEROS

Distinct19
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.377263046
Minimum0
Maximum43
Zeros1582
Zeros (%)42.1%
Negative0
Negative (%)0.0%
Memory size29.5 KiB
2022-05-11T20:34:06.445822image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile5
Maximum43
Range43
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.041540518
Coefficient of variation (CV)1.482317067
Kurtosis63.74365948
Mean1.377263046
Median Absolute Deviation (MAD)1
Skewness4.94941434
Sum5173
Variance4.167887687
MonotonicityNot monotonic
2022-05-11T20:34:06.695817image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
01582
42.1%
1955
25.4%
2533
 
14.2%
3294
 
7.8%
4163
 
4.3%
576
 
2.0%
657
 
1.5%
832
 
0.9%
730
 
0.8%
911
 
0.3%
Other values (9)23
 
0.6%
ValueCountFrequency (%)
01582
42.1%
1955
25.4%
2533
 
14.2%
3294
 
7.8%
4163
 
4.3%
576
 
2.0%
657
 
1.5%
730
 
0.8%
832
 
0.9%
911
 
0.3%
ValueCountFrequency (%)
431
 
< 0.1%
311
 
< 0.1%
191
 
< 0.1%
154
 
0.1%
141
 
< 0.1%
131
 
< 0.1%
123
 
0.1%
115
0.1%
106
0.2%
911
0.3%

plot_keywords
Categorical

HIGH CARDINALITY
UNIFORM

Distinct3656
Distinct (%)97.3%
Missing0
Missing (%)0.0%
Memory size29.5 KiB
alien friendship|alien invasion|australia|flying car|mother daughter relationship
 
3
1940s|child hero|fantasy world|orphan|reference to peter pan
 
3
animal name in title|ape abducts a woman|gorilla|island|king kong
 
3
halloween|masked killer|michael myers|slasher|trick or treat
 
3
assistant|experiment|frankenstein|medical student|scientist
 
3
Other values (3651)
3741 

Length

Max length149
Median length98
Mean length52.49813632
Min length6

Characters and Unicode

Total characters197183
Distinct characters42
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3562 ?
Unique (%)94.8%

Sample

1st rowavatar|future|marine|native|paraplegic
2nd rowgoddess|marriage ceremony|marriage proposal|pirate|singapore
3rd rowbomb|espionage|sequel|spy|terrorist
4th rowdeception|imprisonment|lawlessness|police officer|terrorist plot
5th rowalien|american civil war|male nipple|mars|princess

Common Values

ValueCountFrequency (%)
alien friendship|alien invasion|australia|flying car|mother daughter relationship3
 
0.1%
1940s|child hero|fantasy world|orphan|reference to peter pan3
 
0.1%
animal name in title|ape abducts a woman|gorilla|island|king kong3
 
0.1%
halloween|masked killer|michael myers|slasher|trick or treat3
 
0.1%
assistant|experiment|frankenstein|medical student|scientist3
 
0.1%
eighteen wheeler|illegal street racing|truck|trucker|undercover cop3
 
0.1%
clone|environment|escape|island|lottery2
 
0.1%
mall|mayhem|nurse|rear entry sex|survival horror2
 
0.1%
19th century|around the world|inventor|martial arts|train2
 
0.1%
autistic child|boy|child in danger|fbi|nsa2
 
0.1%
Other values (3646)3730
99.3%

Length

2022-05-11T20:34:07.055182image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
in213
 
1.6%
of171
 
1.3%
on157
 
1.2%
a151
 
1.1%
the148
 
1.1%
to126
 
0.9%
york93
 
0.7%
female80
 
0.6%
based78
 
0.6%
by68
 
0.5%
Other values (9008)12364
90.6%

Most occurring characters

ValueCountFrequency (%)
e19076
 
9.7%
a15089
 
7.7%
|14958
 
7.6%
i14318
 
7.3%
r13997
 
7.1%
t12338
 
6.3%
n11994
 
6.1%
o11955
 
6.1%
s10231
 
5.2%
9893
 
5.0%
Other values (32)63334
32.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter171292
86.9%
Math Symbol14958
 
7.6%
Space Separator9893
 
5.0%
Decimal Number856
 
0.4%
Other Punctuation182
 
0.1%
Open Punctuation1
 
< 0.1%
Close Punctuation1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e19076
11.1%
a15089
 
8.8%
i14318
 
8.4%
r13997
 
8.2%
t12338
 
7.2%
n11994
 
7.0%
o11955
 
7.0%
s10231
 
6.0%
l8540
 
5.0%
c7392
 
4.3%
Other values (16)46362
27.1%
Decimal Number
ValueCountFrequency (%)
1221
25.8%
0195
22.8%
9176
20.6%
250
 
5.8%
849
 
5.7%
540
 
4.7%
738
 
4.4%
631
 
3.6%
330
 
3.5%
426
 
3.0%
Other Punctuation
ValueCountFrequency (%)
.112
61.5%
'70
38.5%
Math Symbol
ValueCountFrequency (%)
|14958
100.0%
Space Separator
ValueCountFrequency (%)
9893
100.0%
Open Punctuation
ValueCountFrequency (%)
(1
100.0%
Close Punctuation
ValueCountFrequency (%)
)1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin171292
86.9%
Common25891
 
13.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e19076
11.1%
a15089
 
8.8%
i14318
 
8.4%
r13997
 
8.2%
t12338
 
7.2%
n11994
 
7.0%
o11955
 
7.0%
s10231
 
6.0%
l8540
 
5.0%
c7392
 
4.3%
Other values (16)46362
27.1%
Common
ValueCountFrequency (%)
|14958
57.8%
9893
38.2%
1221
 
0.9%
0195
 
0.8%
9176
 
0.7%
.112
 
0.4%
'70
 
0.3%
250
 
0.2%
849
 
0.2%
540
 
0.2%
Other values (6)127
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII197183
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e19076
 
9.7%
a15089
 
7.7%
|14958
 
7.6%
i14318
 
7.3%
r13997
 
7.1%
t12338
 
6.3%
n11994
 
6.1%
o11955
 
6.1%
s10231
 
5.2%
9893
 
5.0%
Other values (32)63334
32.1%

movie_imdb_link
Categorical

HIGH CARDINALITY
UNIFORM

Distinct3656
Distinct (%)97.3%
Missing0
Missing (%)0.0%
Memory size29.5 KiB
http://www.imdb.com/title/tt2224026/?ref_=fn_tt_tt_1
 
3
http://www.imdb.com/title/tt3332064/?ref_=fn_tt_tt_1
 
3
http://www.imdb.com/title/tt0360717/?ref_=fn_tt_tt_1
 
3
http://www.imdb.com/title/tt0077651/?ref_=fn_tt_tt_1
 
3
http://www.imdb.com/title/tt1976009/?ref_=fn_tt_tt_1
 
3
Other values (3651)
3741 

Length

Max length52
Median length52
Mean length52
Min length52

Characters and Unicode

Total characters195312
Distinct characters31
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3562 ?
Unique (%)94.8%

Sample

1st rowhttp://www.imdb.com/title/tt0499549/?ref_=fn_tt_tt_1
2nd rowhttp://www.imdb.com/title/tt0449088/?ref_=fn_tt_tt_1
3rd rowhttp://www.imdb.com/title/tt2379713/?ref_=fn_tt_tt_1
4th rowhttp://www.imdb.com/title/tt1345836/?ref_=fn_tt_tt_1
5th rowhttp://www.imdb.com/title/tt0401729/?ref_=fn_tt_tt_1

Common Values

ValueCountFrequency (%)
http://www.imdb.com/title/tt2224026/?ref_=fn_tt_tt_13
 
0.1%
http://www.imdb.com/title/tt3332064/?ref_=fn_tt_tt_13
 
0.1%
http://www.imdb.com/title/tt0360717/?ref_=fn_tt_tt_13
 
0.1%
http://www.imdb.com/title/tt0077651/?ref_=fn_tt_tt_13
 
0.1%
http://www.imdb.com/title/tt1976009/?ref_=fn_tt_tt_13
 
0.1%
http://www.imdb.com/title/tt0232500/?ref_=fn_tt_tt_13
 
0.1%
http://www.imdb.com/title/tt0399201/?ref_=fn_tt_tt_12
 
0.1%
http://www.imdb.com/title/tt0363547/?ref_=fn_tt_tt_12
 
0.1%
http://www.imdb.com/title/tt0327437/?ref_=fn_tt_tt_12
 
0.1%
http://www.imdb.com/title/tt0120749/?ref_=fn_tt_tt_12
 
0.1%
Other values (3646)3730
99.3%

Length

2022-05-11T20:34:07.414550image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
http://www.imdb.com/title/tt2224026/?ref_=fn_tt_tt_13
 
0.1%
http://www.imdb.com/title/tt0360717/?ref_=fn_tt_tt_13
 
0.1%
http://www.imdb.com/title/tt0077651/?ref_=fn_tt_tt_13
 
0.1%
http://www.imdb.com/title/tt1976009/?ref_=fn_tt_tt_13
 
0.1%
http://www.imdb.com/title/tt0232500/?ref_=fn_tt_tt_13
 
0.1%
http://www.imdb.com/title/tt3332064/?ref_=fn_tt_tt_13
 
0.1%
http://www.imdb.com/title/tt2058673/?ref_=fn_tt_tt_12
 
0.1%
http://www.imdb.com/title/tt1939659/?ref_=fn_tt_tt_12
 
0.1%
http://www.imdb.com/title/tt2053463/?ref_=fn_tt_tt_12
 
0.1%
http://www.imdb.com/title/tt1099212/?ref_=fn_tt_tt_12
 
0.1%
Other values (3646)3730
99.3%

Most occurring characters

ValueCountFrequency (%)
t37560
19.2%
/18780
 
9.6%
_15024
 
7.7%
w11268
 
5.8%
e7512
 
3.8%
f7512
 
3.8%
.7512
 
3.8%
i7512
 
3.8%
m7512
 
3.8%
17504
 
3.8%
Other values (21)67616
34.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter112680
57.7%
Other Punctuation33804
 
17.3%
Decimal Number30048
 
15.4%
Connector Punctuation15024
 
7.7%
Math Symbol3756
 
1.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t37560
33.3%
w11268
 
10.0%
e7512
 
6.7%
f7512
 
6.7%
i7512
 
6.7%
m7512
 
6.7%
r3756
 
3.3%
n3756
 
3.3%
h3756
 
3.3%
l3756
 
3.3%
Other values (5)18780
16.7%
Decimal Number
ValueCountFrequency (%)
17504
25.0%
05171
17.2%
22717
 
9.0%
32385
 
7.9%
42296
 
7.6%
82136
 
7.1%
92055
 
6.8%
71994
 
6.6%
61967
 
6.5%
51823
 
6.1%
Other Punctuation
ValueCountFrequency (%)
/18780
55.6%
.7512
 
22.2%
?3756
 
11.1%
:3756
 
11.1%
Connector Punctuation
ValueCountFrequency (%)
_15024
100.0%
Math Symbol
ValueCountFrequency (%)
=3756
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin112680
57.7%
Common82632
42.3%

Most frequent character per script

Common
ValueCountFrequency (%)
/18780
22.7%
_15024
18.2%
.7512
 
9.1%
17504
 
9.1%
05171
 
6.3%
?3756
 
4.5%
=3756
 
4.5%
:3756
 
4.5%
22717
 
3.3%
32385
 
2.9%
Other values (6)12271
14.9%
Latin
ValueCountFrequency (%)
t37560
33.3%
w11268
 
10.0%
e7512
 
6.7%
f7512
 
6.7%
i7512
 
6.7%
m7512
 
6.7%
r3756
 
3.3%
n3756
 
3.3%
h3756
 
3.3%
l3756
 
3.3%
Other values (5)18780
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII195312
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t37560
19.2%
/18780
 
9.6%
_15024
 
7.7%
w11268
 
5.8%
e7512
 
3.8%
f7512
 
3.8%
.7512
 
3.8%
i7512
 
3.8%
m7512
 
3.8%
17504
 
3.8%
Other values (21)67616
34.6%

num_user_for_reviews
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct940
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean336.8431842
Minimum4
Maximum5060
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size29.5 KiB
2022-05-11T20:34:07.711416image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile36
Q1110
median210
Q3398.25
95-th percentile1044.5
Maximum5060
Range5056
Interquartile range (IQR)288.25

Descriptive statistics

Standard deviation411.2273684
Coefficient of variation (CV)1.220827339
Kurtosis22.48974413
Mean336.8431842
Median Absolute Deviation (MAD)122
Skewness3.844203716
Sum1265183
Variance169107.9485
MonotonicityNot monotonic
2022-05-11T20:34:08.071024image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5018
 
0.5%
12617
 
0.5%
2617
 
0.5%
10016
 
0.4%
16216
 
0.4%
18116
 
0.4%
8815
 
0.4%
8415
 
0.4%
19415
 
0.4%
13215
 
0.4%
Other values (930)3596
95.7%
ValueCountFrequency (%)
41
 
< 0.1%
53
0.1%
62
 
0.1%
71
 
< 0.1%
81
 
< 0.1%
91
 
< 0.1%
106
0.2%
114
0.1%
125
0.1%
132
 
0.1%
ValueCountFrequency (%)
50601
< 0.1%
46671
< 0.1%
41441
< 0.1%
36461
< 0.1%
35971
< 0.1%
35161
< 0.1%
34001
< 0.1%
32861
< 0.1%
31891
< 0.1%
30541
< 0.1%

language
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct34
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size29.5 KiB
English
3598 
French
 
34
Spanish
 
23
Mandarin
 
15
German
 
10
Other values (29)
 
76

Length

Max length10
Median length7
Mean length6.99627263
Min length4

Characters and Unicode

Total characters26278
Distinct characters40
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique15 ?
Unique (%)0.4%

Sample

1st rowEnglish
2nd rowEnglish
3rd rowEnglish
4th rowEnglish
5th rowEnglish

Common Values

ValueCountFrequency (%)
English3598
95.8%
French34
 
0.9%
Spanish23
 
0.6%
Mandarin15
 
0.4%
German10
 
0.3%
Japanese10
 
0.3%
Cantonese7
 
0.2%
Italian7
 
0.2%
Portuguese5
 
0.1%
Hindi5
 
0.1%
Other values (24)42
 
1.1%

Length

2022-05-11T20:34:08.400455image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
english3598
95.8%
french34
 
0.9%
spanish23
 
0.6%
mandarin15
 
0.4%
german10
 
0.3%
japanese10
 
0.3%
cantonese7
 
0.2%
italian7
 
0.2%
korean5
 
0.1%
portuguese5
 
0.1%
Other values (24)42
 
1.1%

Most occurring characters

ValueCountFrequency (%)
n3766
14.3%
i3685
14.0%
h3666
14.0%
s3655
13.9%
g3611
13.7%
l3610
13.7%
E3598
13.7%
a143
 
0.5%
e109
 
0.4%
r84
 
0.3%
Other values (30)351
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter22522
85.7%
Uppercase Letter3756
 
14.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n3766
16.7%
i3685
16.4%
h3666
16.3%
s3655
16.2%
g3611
16.0%
l3610
16.0%
a143
 
0.6%
e109
 
0.5%
r84
 
0.4%
c40
 
0.2%
Other values (11)153
 
0.7%
Uppercase Letter
ValueCountFrequency (%)
E3598
95.8%
F35
 
0.9%
S23
 
0.6%
M17
 
0.5%
J10
 
0.3%
G10
 
0.3%
I9
 
0.2%
D8
 
0.2%
C8
 
0.2%
P8
 
0.2%
Other values (9)30
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
Latin26278
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n3766
14.3%
i3685
14.0%
h3666
14.0%
s3655
13.9%
g3611
13.7%
l3610
13.7%
E3598
13.7%
a143
 
0.5%
e109
 
0.4%
r84
 
0.3%
Other values (30)351
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII26278
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n3766
14.3%
i3685
14.0%
h3666
14.0%
s3655
13.9%
g3611
13.7%
l3610
13.7%
E3598
13.7%
a143
 
0.5%
e109
 
0.4%
r84
 
0.3%
Other values (30)351
 
1.3%

country
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct45
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size29.5 KiB
USA
2987 
UK
318 
France
 
101
Germany
 
80
Canada
 
59
Other values (40)
 
211

Length

Max length14
Median length3
Mean length3.375665602
Min length2

Characters and Unicode

Total characters12679
Distinct characters45
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)0.4%

Sample

1st rowUSA
2nd rowUSA
3rd rowUK
4th rowUSA
5th rowUSA

Common Values

ValueCountFrequency (%)
USA2987
79.5%
UK318
 
8.5%
France101
 
2.7%
Germany80
 
2.1%
Canada59
 
1.6%
Australia39
 
1.0%
Spain21
 
0.6%
Japan15
 
0.4%
Hong Kong13
 
0.3%
China13
 
0.3%
Other values (35)110
 
2.9%

Length

2022-05-11T20:34:08.822324image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
usa2987
78.7%
uk318
 
8.4%
france101
 
2.7%
germany81
 
2.1%
canada59
 
1.6%
australia39
 
1.0%
spain21
 
0.6%
japan15
 
0.4%
hong13
 
0.3%
kong13
 
0.3%
Other values (40)150
 
4.0%

Most occurring characters

ValueCountFrequency (%)
U3305
26.1%
A3034
23.9%
S3019
23.8%
a616
 
4.9%
n379
 
3.0%
K339
 
2.7%
r273
 
2.2%
e262
 
2.1%
i120
 
0.9%
c119
 
0.9%
Other values (35)1213
 
9.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter10088
79.6%
Lowercase Letter2550
 
20.1%
Space Separator41
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a616
24.2%
n379
14.9%
r273
10.7%
e262
10.3%
i120
 
4.7%
c119
 
4.7%
y98
 
3.8%
d93
 
3.6%
m93
 
3.6%
l91
 
3.6%
Other values (13)406
15.9%
Uppercase Letter
ValueCountFrequency (%)
U3305
32.8%
A3034
30.1%
S3019
29.9%
K339
 
3.4%
F102
 
1.0%
G83
 
0.8%
C77
 
0.8%
I30
 
0.3%
N19
 
0.2%
H15
 
0.1%
Other values (11)65
 
0.6%
Space Separator
ValueCountFrequency (%)
41
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin12638
99.7%
Common41
 
0.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
U3305
26.2%
A3034
24.0%
S3019
23.9%
a616
 
4.9%
n379
 
3.0%
K339
 
2.7%
r273
 
2.2%
e262
 
2.1%
i120
 
0.9%
c119
 
0.9%
Other values (34)1172
 
9.3%
Common
ValueCountFrequency (%)
41
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII12679
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
U3305
26.1%
A3034
23.9%
S3019
23.8%
a616
 
4.9%
n379
 
3.0%
K339
 
2.7%
r273
 
2.2%
e262
 
2.1%
i120
 
0.9%
c119
 
0.9%
Other values (35)1213
 
9.6%

content_rating
Categorical

HIGH CORRELATION

Distinct12
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size29.5 KiB
R
1700 
PG-13
1308 
PG
566 
G
 
87
Not Rated
 
34
Other values (7)
 
61

Length

Max length9
Median length8
Mean length2.693556976
Min length1

Characters and Unicode

Total characters10117
Distinct characters24
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowPG-13
2nd rowPG-13
3rd rowPG-13
4th rowPG-13
5th rowPG-13

Common Values

ValueCountFrequency (%)
R1700
45.3%
PG-131308
34.8%
PG566
 
15.1%
G87
 
2.3%
Not Rated34
 
0.9%
Unrated22
 
0.6%
Approved17
 
0.5%
X10
 
0.3%
NC-176
 
0.2%
Passed3
 
0.1%
Other values (2)3
 
0.1%

Length

2022-05-11T20:34:09.119188image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
r1700
44.9%
pg-131308
34.5%
pg566
 
14.9%
g87
 
2.3%
not34
 
0.9%
rated34
 
0.9%
unrated22
 
0.6%
approved17
 
0.4%
x10
 
0.3%
nc-176
 
0.2%
Other values (3)6
 
0.2%

Most occurring characters

ValueCountFrequency (%)
G1962
19.4%
P1878
18.6%
R1734
17.1%
-1314
13.0%
11314
13.0%
31308
12.9%
t90
 
0.9%
e76
 
0.8%
d76
 
0.8%
a59
 
0.6%
Other values (14)306
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter5671
56.1%
Decimal Number2628
26.0%
Dash Punctuation1314
 
13.0%
Lowercase Letter470
 
4.6%
Space Separator34
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t90
19.1%
e76
16.2%
d76
16.2%
a59
12.6%
o51
10.9%
r39
8.3%
p34
 
7.2%
n22
 
4.7%
v17
 
3.6%
s6
 
1.3%
Uppercase Letter
ValueCountFrequency (%)
G1962
34.6%
P1878
33.1%
R1734
30.6%
N40
 
0.7%
U22
 
0.4%
A17
 
0.3%
X10
 
0.2%
C6
 
0.1%
M2
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
11314
50.0%
31308
49.8%
76
 
0.2%
Dash Punctuation
ValueCountFrequency (%)
-1314
100.0%
Space Separator
ValueCountFrequency (%)
34
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin6141
60.7%
Common3976
39.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
G1962
31.9%
P1878
30.6%
R1734
28.2%
t90
 
1.5%
e76
 
1.2%
d76
 
1.2%
a59
 
1.0%
o51
 
0.8%
N40
 
0.7%
r39
 
0.6%
Other values (9)136
 
2.2%
Common
ValueCountFrequency (%)
-1314
33.0%
11314
33.0%
31308
32.9%
34
 
0.9%
76
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII10117
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
G1962
19.4%
P1878
18.6%
R1734
17.1%
-1314
13.0%
11314
13.0%
31308
12.9%
t90
 
0.9%
e76
 
0.8%
d76
 
0.8%
a59
 
0.6%
Other values (14)306
 
3.0%

budget
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct359
Distinct (%)9.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46236849.64
Minimum218
Maximum1.22155 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size29.5 KiB
2022-05-11T20:34:09.462932image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum218
5-th percentile1200000
Q110000000
median25000000
Q350000000
95-th percentile140000000
Maximum1.22155 × 1010
Range1.221549978 × 1010
Interquartile range (IQR)40000000

Descriptive statistics

Standard deviation226010288.5
Coefficient of variation (CV)4.888098784
Kurtosis2278.421463
Mean46236849.64
Median Absolute Deviation (MAD)17000000
Skewness44.17414414
Sum1.736656072 × 1011
Variance5.10806505 × 1016
MonotonicityNot monotonic
2022-05-11T20:34:09.853550image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20000000157
 
4.2%
30000000134
 
3.6%
15000000132
 
3.5%
40000000130
 
3.5%
25000000126
 
3.4%
35000000117
 
3.1%
10000000105
 
2.8%
50000000100
 
2.7%
6000000090
 
2.4%
1200000076
 
2.0%
Other values (349)2589
68.9%
ValueCountFrequency (%)
2181
< 0.1%
11001
< 0.1%
45001
< 0.1%
70002
0.1%
100002
0.1%
140001
< 0.1%
150001
< 0.1%
230001
< 0.1%
250002
0.1%
400002
0.1%
ValueCountFrequency (%)
1.22155 × 10101
< 0.1%
42000000001
< 0.1%
25000000001
< 0.1%
24000000001
< 0.1%
21275198981
< 0.1%
11000000001
< 0.1%
10000000001
< 0.1%
7000000002
0.1%
5536320001
< 0.1%
4000000001
< 0.1%

title_year
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct74
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2002.976571
Minimum1927
Maximum2016
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size29.5 KiB
2022-05-11T20:34:10.212914image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1927
5-th percentile1985
Q11999
median2004
Q32010
95-th percentile2014
Maximum2016
Range89
Interquartile range (IQR)11

Descriptive statistics

Standard deviation9.888108209
Coefficient of variation (CV)0.004936706876
Kurtosis8.291659729
Mean2002.976571
Median Absolute Deviation (MAD)5
Skewness-2.070019489
Sum7523180
Variance97.77468395
MonotonicityNot monotonic
2022-05-11T20:34:10.603528image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2002190
 
5.1%
2006189
 
5.0%
2009182
 
4.8%
2008182
 
4.8%
2005182
 
4.8%
2004181
 
4.8%
2001179
 
4.8%
2010168
 
4.5%
2011168
 
4.5%
2013163
 
4.3%
Other values (64)1972
52.5%
ValueCountFrequency (%)
19271
< 0.1%
19291
< 0.1%
19331
< 0.1%
19351
< 0.1%
19361
< 0.1%
19371
< 0.1%
19392
0.1%
19401
< 0.1%
19462
0.1%
19471
< 0.1%
ValueCountFrequency (%)
201659
 
1.6%
2015128
3.4%
2014145
3.9%
2013163
4.3%
2012158
4.2%
2011168
4.5%
2010168
4.5%
2009182
4.8%
2008182
4.8%
2007152
4.0%

actor_2_facebook_likes
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct855
Distinct (%)22.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2021.775825
Minimum0
Maximum137000
Zeros11
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size29.5 KiB
2022-05-11T20:34:10.962896image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile63
Q1384.75
median685.5
Q3976
95-th percentile12000
Maximum137000
Range137000
Interquartile range (IQR)591.25

Descriptive statistics

Standard deviation4544.908236
Coefficient of variation (CV)2.247978326
Kurtosis211.6549776
Mean2021.775825
Median Absolute Deviation (MAD)293.5
Skewness9.010297621
Sum7593790
Variance20656190.87
MonotonicityNot monotonic
2022-05-11T20:34:11.275390image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1000278
 
7.4%
11000107
 
2.8%
200090
 
2.4%
300072
 
1.9%
1000045
 
1.2%
1300039
 
1.0%
1400039
 
1.0%
82632
 
0.9%
400030
 
0.8%
1200028
 
0.7%
Other values (845)2996
79.8%
ValueCountFrequency (%)
011
0.3%
25
0.1%
36
0.2%
43
 
0.1%
54
 
0.1%
63
 
0.1%
71
 
< 0.1%
84
 
0.1%
95
0.1%
104
 
0.1%
ValueCountFrequency (%)
1370001
 
< 0.1%
290001
 
< 0.1%
270002
 
0.1%
250003
 
0.1%
230006
0.2%
2200011
0.3%
210003
 
0.1%
200006
0.2%
190007
0.2%
180009
0.2%

imdb_score
Real number (ℝ≥0)

HIGH CORRELATION

Distinct74
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.465282215
Minimum1.6
Maximum9.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size29.5 KiB
2022-05-11T20:34:11.572253image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1.6
5-th percentile4.6
Q15.9
median6.6
Q37.2
95-th percentile8
Maximum9.3
Range7.7
Interquartile range (IQR)1.3

Descriptive statistics

Standard deviation1.056246753
Coefficient of variation (CV)0.1633721032
Kurtosis1.146984546
Mean6.465282215
Median Absolute Deviation (MAD)0.7
Skewness-0.7233267987
Sum24283.6
Variance1.115657204
MonotonicityNot monotonic
2022-05-11T20:34:11.947245image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.7176
 
4.7%
6.6162
 
4.3%
6.5154
 
4.1%
6.4146
 
3.9%
6.8143
 
3.8%
6.1142
 
3.8%
7.1140
 
3.7%
7140
 
3.7%
7.2139
 
3.7%
6.9135
 
3.6%
Other values (64)2279
60.7%
ValueCountFrequency (%)
1.61
 
< 0.1%
1.92
 
0.1%
21
 
< 0.1%
2.13
0.1%
2.21
 
< 0.1%
2.33
0.1%
2.42
 
0.1%
2.51
 
< 0.1%
2.74
0.1%
2.85
0.1%
ValueCountFrequency (%)
9.31
 
< 0.1%
9.21
 
< 0.1%
92
 
0.1%
8.94
 
0.1%
8.85
 
0.1%
8.77
 
0.2%
8.68
 
0.2%
8.519
0.5%
8.414
0.4%
8.325
0.7%

aspect_ratio
Real number (ℝ≥0)

Distinct18
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.111014377
Minimum1.18
Maximum16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size29.5 KiB
2022-05-11T20:34:12.244117image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1.18
5-th percentile1.85
Q11.85
median2.35
Q32.35
95-th percentile2.35
Maximum16
Range14.82
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.3530679822
Coefficient of variation (CV)0.1672503921
Kurtosis636.4138366
Mean2.111014377
Median Absolute Deviation (MAD)0
Skewness16.01440583
Sum7928.97
Variance0.1246570001
MonotonicityNot monotonic
2022-05-11T20:34:12.494107image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
2.351988
52.9%
1.851590
42.3%
1.3748
 
1.3%
1.6639
 
1.0%
1.7834
 
0.9%
1.3318
 
0.5%
2.211
 
0.3%
2.3911
 
0.3%
23
 
0.1%
2.43
 
0.1%
Other values (8)11
 
0.3%
ValueCountFrequency (%)
1.181
 
< 0.1%
1.3318
 
0.5%
1.3748
 
1.3%
1.51
 
< 0.1%
1.6639
 
1.0%
1.752
 
0.1%
1.771
 
< 0.1%
1.7834
 
0.9%
1.851590
42.3%
23
 
0.1%
ValueCountFrequency (%)
161
 
< 0.1%
2.763
 
0.1%
2.551
 
< 0.1%
2.43
 
0.1%
2.3911
 
0.3%
2.351988
52.9%
2.241
 
< 0.1%
2.211
 
0.3%
23
 
0.1%
1.851590
42.3%

movie_facebook_likes
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct657
Distinct (%)17.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9353.82934
Minimum0
Maximum349000
Zeros1742
Zeros (%)46.4%
Negative0
Negative (%)0.0%
Memory size29.5 KiB
2022-05-11T20:34:12.759727image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median227
Q311000
95-th percentile48000
Maximum349000
Range349000
Interquartile range (IQR)11000

Descriptive statistics

Standard deviation21462.88912
Coefficient of variation (CV)2.294556416
Kurtosis33.40142354
Mean9353.82934
Median Absolute Deviation (MAD)227
Skewness4.516866747
Sum35132983
Variance460655609.5
MonotonicityNot monotonic
2022-05-11T20:34:13.087844image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01742
46.4%
100084
 
2.2%
1100074
 
2.0%
1000068
 
1.8%
1200053
 
1.4%
1500050
 
1.3%
1300049
 
1.3%
1400046
 
1.2%
1600044
 
1.2%
200038
 
1.0%
Other values (647)1508
40.1%
ValueCountFrequency (%)
01742
46.4%
121
 
< 0.1%
191
 
< 0.1%
261
 
< 0.1%
271
 
< 0.1%
303
 
0.1%
321
 
< 0.1%
401
 
< 0.1%
421
 
< 0.1%
471
 
< 0.1%
ValueCountFrequency (%)
3490001
< 0.1%
1990001
< 0.1%
1970001
< 0.1%
1910001
< 0.1%
1900001
< 0.1%
1750001
< 0.1%
1650001
< 0.1%
1640001
< 0.1%
1530001
< 0.1%
1500001
< 0.1%

Interactions

2022-05-11T20:33:47.952203image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:32:19.380377image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:32:25.013529image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:32:30.430233image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:32:36.005575image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:32:40.699323image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:32:46.301085image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:32:52.087291image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:32:57.444124image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:03.537955image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:09.128914image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:14.484829image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:19.829585image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:25.741711image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:30.771132image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:36.368994image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:42.424600image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:48.277799image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:32:19.840721image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:32:25.350977image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:32:30.732608image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:32:36.299083image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:32:40.950353image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:32:46.613371image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:32:52.412617image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:32:57.754637image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:03.824396image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:09.374403image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:14.782829image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:20.532049image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:26.024128image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:31.093760image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:36.681855image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:42.747061image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:48.623723image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:32:20.192036image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:32:25.707598image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:32:31.080652image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:32:36.884158image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:32:41.261032image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:32:46.971996image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:32:52.727016image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:32:58.107076image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:04.176430image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:09.778475image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:15.124780image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:20.878662image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:26.339301image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:31.421723image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:37.063148image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:43.110704image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:48.915237image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:32:20.502303image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:32:26.032544image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:32:31.399462image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:32:37.219968image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:32:41.578236image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:32:47.284704image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:32:53.016035image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:32:58.487632image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:04.494075image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:10.078357image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:15.441689image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:21.203522image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:26.645055image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:31.745833image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:37.366636image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:43.428479image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:49.182390image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:32:20.808602image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:32:26.325894image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:32:31.687008image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:32:37.568891image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:32:41.883351image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:32:47.592183image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:32:53.326646image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:32:58.796732image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:04.795993image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:10.367707image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:15.737017image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:21.497453image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:26.931634image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:32.069091image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:37.675468image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:43.739594image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:49.474801image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:32:21.115312image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:32:26.594463image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:32:31.967680image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:32:37.896733image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:32:42.168113image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:32:48.164577image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:32:53.623114image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:32:59.111425image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:05.079181image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:10.657081image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:16.018011image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:21.825636image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:27.214645image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:32.383934image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:38.060132image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:44.038010image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:49.789006image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:32:21.458002image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:32:26.866653image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:32:32.267323image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:32:38.255904image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:32:42.554768image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:32:48.500017image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:32:53.949736image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:32:59.493998image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:05.493021image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:11.000289image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:16.331772image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:22.145064image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:27.489635image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:32.741827image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:38.371363image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:44.337353image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:50.123532image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:32:21.765512image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:32:27.130972image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:32:32.558947image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:32:38.610881image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:32:43.003721image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:32:48.823694image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:32:54.267789image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:32:59.822672image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:05.810309image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:11.321100image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:16.659380image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:22.458213image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:27.756206image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:33.068891image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:38.704024image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:44.690148image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:50.461858image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:32:22.097961image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:32:27.425833image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:32:32.824138image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:32:38.895713image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:32:43.360196image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:32:49.166724image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:32:54.551073image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:00.157786image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:06.172597image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:11.656043image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:16.987274image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:22.783361image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:28.039264image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:33.426723image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:39.076858image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:45.072105image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:50.813216image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:32:22.428128image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:32:27.757948image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:32:33.112266image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:32:39.120519image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:32:43.704581image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:32:49.492670image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:32:54.822467image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:00.513556image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:06.531508image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:11.963532image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:17.325875image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:23.115849image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:28.339956image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:33.762051image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:39.420116image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:45.385657image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:51.129092image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:32:22.734393image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:32:28.091423image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:32:33.400411image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:32:39.299753image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:32:44.154284image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:32:49.800174image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:32:55.140174image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:00.849788image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:06.841172image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:12.252341image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:17.631658image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:23.445882image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:28.626939image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:34.064214image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:39.713925image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:45.690214image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:51.451782image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:32:23.023643image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:32:28.410075image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:32:33.714604image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:32:39.481374image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:32:44.465180image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:32:50.131833image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:32:55.451105image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:01.170222image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:07.141796image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:12.545699image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:17.932116image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:23.743858image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:28.934005image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:34.366151image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:39.995515image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:46.025813image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:51.755516image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:32:23.343584image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:32:28.756555image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:32:34.058570image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:32:39.676375image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:32:44.761674image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:32:50.463795image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:32:55.795861image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:01.511542image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:07.484938image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:12.855211image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:18.234262image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:24.067634image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:29.228257image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:34.702011image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:40.338276image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:46.315760image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:52.075186image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:32:23.650858image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:32:29.054166image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:32:34.400020image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:32:39.848311image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:32:45.073046image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:32:50.768200image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:32:56.090857image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:01.848410image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:07.789265image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:13.167140image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:18.510059image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:24.389068image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:29.521105image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:35.012511image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:40.638851image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:46.629330image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:52.421169image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:32:24.005170image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:32:29.376571image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:32:34.828190image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:32:40.031749image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:32:45.391510image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:32:51.112903image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:32:56.419684image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:02.500275image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:08.138846image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:13.500957image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:18.855308image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:24.725850image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:29.824895image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:35.362651image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:40.928486image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:46.959687image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:52.754948image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:32:24.343318image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:32:29.716870image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:32:35.214260image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:32:40.243312image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:32:45.684106image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:32:51.426145image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:32:56.729593image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:02.855365image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:08.484451image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:13.836797image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:19.167298image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:25.084514image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:30.138968image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:35.717433image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:41.262085image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:47.286915image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:53.092131image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:32:24.688644image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:32:30.069371image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:32:35.604949image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:32:40.454046image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:32:45.984100image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:32:51.746935image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:32:57.062664image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:03.250117image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:08.845879image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:14.155436image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:19.488828image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:25.400038image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:30.461503image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:36.044693image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:42.129750image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-11T20:33:47.605841image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2022-05-11T20:34:13.447216image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-05-11T20:34:14.087821image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-05-11T20:34:14.759679image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-05-11T20:34:15.337795image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-05-11T20:34:15.697160image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-05-11T20:33:53.807336image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-05-11T20:33:56.065152image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

df_indexcolordirector_namenum_critic_for_reviewsdurationdirector_facebook_likesactor_3_facebook_likesactor_2_nameactor_1_facebook_likesgrossgenresactor_1_namemovie_titlenum_voted_userscast_total_facebook_likesactor_3_namefacenumber_in_posterplot_keywordsmovie_imdb_linknum_user_for_reviewslanguagecountrycontent_ratingbudgettitle_yearactor_2_facebook_likesimdb_scoreaspect_ratiomovie_facebook_likes
00ColorJames Cameron723.0178.00.0855.0Joel David Moore1000.0760505847.0Action|Adventure|Fantasy|Sci-FiCCH PounderAvatar8862044834Wes Studi0.0avatar|future|marine|native|paraplegichttp://www.imdb.com/title/tt0499549/?ref_=fn_tt_tt_13054.0EnglishUSAPG-13237000000.02009.0936.07.91.7833000
11ColorGore Verbinski302.0169.0563.01000.0Orlando Bloom40000.0309404152.0Action|Adventure|FantasyJohnny DeppPirates of the Caribbean: At World's End47122048350Jack Davenport0.0goddess|marriage ceremony|marriage proposal|pirate|singaporehttp://www.imdb.com/title/tt0449088/?ref_=fn_tt_tt_11238.0EnglishUSAPG-13300000000.02007.05000.07.12.350
22ColorSam Mendes602.0148.00.0161.0Rory Kinnear11000.0200074175.0Action|Adventure|ThrillerChristoph WaltzSpectre27586811700Stephanie Sigman1.0bomb|espionage|sequel|spy|terroristhttp://www.imdb.com/title/tt2379713/?ref_=fn_tt_tt_1994.0EnglishUKPG-13245000000.02015.0393.06.82.3585000
33ColorChristopher Nolan813.0164.022000.023000.0Christian Bale27000.0448130642.0Action|ThrillerTom HardyThe Dark Knight Rises1144337106759Joseph Gordon-Levitt0.0deception|imprisonment|lawlessness|police officer|terrorist plothttp://www.imdb.com/title/tt1345836/?ref_=fn_tt_tt_12701.0EnglishUSAPG-13250000000.02012.023000.08.52.35164000
45ColorAndrew Stanton462.0132.0475.0530.0Samantha Morton640.073058679.0Action|Adventure|Sci-FiDaryl SabaraJohn Carter2122041873Polly Walker1.0alien|american civil war|male nipple|mars|princesshttp://www.imdb.com/title/tt0401729/?ref_=fn_tt_tt_1738.0EnglishUSAPG-13263700000.02012.0632.06.62.3524000
56ColorSam Raimi392.0156.00.04000.0James Franco24000.0336530303.0Action|Adventure|RomanceJ.K. SimmonsSpider-Man 338305646055Kirsten Dunst0.0sandman|spider man|symbiote|venom|villainhttp://www.imdb.com/title/tt0413300/?ref_=fn_tt_tt_11902.0EnglishUSAPG-13258000000.02007.011000.06.22.350
67ColorNathan Greno324.0100.015.0284.0Donna Murphy799.0200807262.0Adventure|Animation|Comedy|Family|Fantasy|Musical|RomanceBrad GarrettTangled2948102036M.C. Gainey1.017th century|based on fairy tale|disney|flower|towerhttp://www.imdb.com/title/tt0398286/?ref_=fn_tt_tt_1387.0EnglishUSAPG260000000.02010.0553.07.81.8529000
78ColorJoss Whedon635.0141.00.019000.0Robert Downey Jr.26000.0458991599.0Action|Adventure|Sci-FiChris HemsworthAvengers: Age of Ultron46266992000Scarlett Johansson4.0artificial intelligence|based on comic book|captain america|marvel cinematic universe|superherohttp://www.imdb.com/title/tt2395427/?ref_=fn_tt_tt_11117.0EnglishUSAPG-13250000000.02015.021000.07.52.35118000
89ColorDavid Yates375.0153.0282.010000.0Daniel Radcliffe25000.0301956980.0Adventure|Family|Fantasy|MysteryAlan RickmanHarry Potter and the Half-Blood Prince32179558753Rupert Grint3.0blood|book|love|potion|professorhttp://www.imdb.com/title/tt0417741/?ref_=fn_tt_tt_1973.0EnglishUKPG250000000.02009.011000.07.52.3510000
910ColorZack Snyder673.0183.00.02000.0Lauren Cohan15000.0330249062.0Action|Adventure|Sci-FiHenry CavillBatman v Superman: Dawn of Justice37163924450Alan D. Purwin0.0based on comic book|batman|sequel to a reboot|superhero|supermanhttp://www.imdb.com/title/tt2975590/?ref_=fn_tt_tt_13018.0EnglishUSAPG-13250000000.02016.04000.06.92.35197000

Last rows

df_indexcolordirector_namenum_critic_for_reviewsdurationdirector_facebook_likesactor_3_facebook_likesactor_2_nameactor_1_facebook_likesgrossgenresactor_1_namemovie_titlenum_voted_userscast_total_facebook_likesactor_3_namefacenumber_in_posterplot_keywordsmovie_imdb_linknum_user_for_reviewslanguagecountrycontent_ratingbudgettitle_yearactor_2_facebook_likesimdb_scoreaspect_ratiomovie_facebook_likes
37465008Black and WhiteKevin Smith136.0102.00.0216.0Brian O'Halloran898.03151130.0ComedyJason MewesClerks1817492103Jeff Anderson4.0clerk|friend|hockey|video|video storehttp://www.imdb.com/title/tt0109445/?ref_=fn_tt_tt_1615.0EnglishUSAR230000.01994.0657.07.81.370
37475011ColorNeil LaBute80.097.0119.07.0Matt Malloy136.02856622.0Comedy|DramaStacy EdwardsIn the Company of Men11550254Jason Dixie0.0business trip|love|misogynist|office|secretaryhttp://www.imdb.com/title/tt0119361/?ref_=fn_tt_tt_1197.0EnglishCanadaR25000.01997.0108.07.31.85489
37485012ColorDavid Ayer233.0109.0453.0120.0Martin Donovan1000.010499968.0Action|Crime|Drama|ThrillerMireille EnosSabotage475021458Maurice Compte3.0dea|drug cartel|kicked in the crotch|strip club|tough girlhttp://www.imdb.com/title/tt1742334/?ref_=fn_tt_tt_1212.0EnglishUSAR35000000.02014.0206.05.71.8510000
37495015Black and WhiteRichard Linklater61.0100.00.00.0Richard Linklater5.01227508.0Comedy|DramaTommy PallottaSlacker151035Jean Caffeine0.0austin texas|moon|pap smear|texas|twenty somethinghttp://www.imdb.com/title/tt0102943/?ref_=fn_tt_tt_180.0EnglishUSAR23000.01991.00.07.11.372000
37505025ColorJohn Waters73.0108.00.0105.0Mink Stole462.0180483.0Comedy|Crime|HorrorDivinePink Flamingos16792760Edith Massey2.0absurd humor|egg|gross out humor|lesbian|sexhttp://www.imdb.com/title/tt0069089/?ref_=fn_tt_tt_1183.0EnglishUSANC-1710000.01972.0143.06.11.370
37515026ColorOlivier Assayas81.0110.0107.045.0Béatrice Dalle576.0136007.0Drama|Music|RomanceMaggie CheungClean3924776Don McKellar1.0jail|junkie|money|motel|singerhttp://www.imdb.com/title/tt0388838/?ref_=fn_tt_tt_139.0FrenchFranceR4500.02004.0133.06.92.35171
37525027ColorJafar Panahi64.090.0397.00.0Nargess Mamizadeh5.0673780.0DramaFereshteh Sadre OrafaiyThe Circle45555Mojgan Faramarzi0.0abortion|bus|hospital|prison|prostitutionhttp://www.imdb.com/title/tt0255094/?ref_=fn_tt_tt_126.0PersianIranNot Rated10000.02000.00.07.51.85697
37535033ColorShane Carruth143.077.0291.08.0David Sullivan291.0424760.0Drama|Sci-Fi|ThrillerShane CarruthPrimer72639368Casey Gooden0.0changing the future|independent film|invention|nonlinear timeline|time travelhttp://www.imdb.com/title/tt0390384/?ref_=fn_tt_tt_1371.0EnglishUSAPG-137000.02004.045.07.01.8519000
37545035ColorRobert Rodriguez56.081.00.06.0Peter Marquardt121.02040920.0Action|Crime|Drama|Romance|ThrillerCarlos GallardoEl Mariachi52055147Consuelo Gómez0.0assassin|death|guitar|gun|mariachihttp://www.imdb.com/title/tt0104815/?ref_=fn_tt_tt_1130.0SpanishUSAR7000.01992.020.06.91.370
37555042ColorJon Gunn43.090.016.016.0Brian Herzlinger86.085222.0DocumentaryJohn AugustMy Date with Drew4285163Jon Gunn0.0actress name in title|crush|date|four word title|video camerahttp://www.imdb.com/title/tt0378407/?ref_=fn_tt_tt_184.0EnglishUSAPG1100.02004.023.06.61.85456